| Zhengzhou Institute of Multipurpose Utilization of Mineral Resources, Chinese Academy of Geological Sciences | Host |
| Citation: |
DANG Weiben, WANG Yubin, WANG Yan, WANG Xin. Condition Optimization of Reduction Roasting Magnetic Separation Technology for Laterite Nickel Ore by BP Neural Network Technique[J]. Conservation and Utilization of Mineral Resources, 2020, 40(5): 128-133. doi: 10.13779/j.cnki.issn1001-0076.2020.05.017
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Condition Optimization of Reduction Roasting Magnetic Separation Technology for Laterite Nickel Ore by BP Neural Network Technique
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Abstract
Reduction roasting magnetic separation process can effectively extract nickel, iron and other valuable metals from laterite nickel ore. Due to the multiple factors existing in the process of reduction roasting magnetic separation of laterite nickel ore, the industrial indicators are unstable. In order to further improve the effect of reduction roasting magnetic separation process in laterite nickel ore, the factors of reducing agent dosage, roasting temperature, material thickness, roasting time and magnetic field intensity were optimized with a nickel ore in Qinghai as raw material by combining orthogonal experiment and BP neural network. The results showed that the optimized experimental conditions by BP neural network model are as follows: dosage of reducing agent 9.5%, roasting temperature 1 070 ℃, layer thickness 10.0 mm, roasting time 65 min and magnetic field strength 2.5 kA·m-1. Under these conditions, a rough nickel concentrate with a yield of 30.29% can be obtained, which is 2.83% higher than the yield of nickel rough concentrate obtained by using the optimal factor combination conditions of the orthogonal test.
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References
[1] 李小明, 白涛涛, 赵俊学, 等.红土镍矿冶炼工艺研究现状及进展[J].材料导报, 2014, 28(5):112-116. [2] 杨涛, 李小明, 赵俊学, 等.红土镍矿处理工艺现状及研究进展[J].有色金属(冶炼部分), 2015(6):9-13. doi: 10.3969/j.issn.1007-7545.2015.06.003 [3] 李长玖, 陈玉明, 黄旭日, 等.镍矿的处理工艺现状及进展[J].矿产综合利用, 2012(6):8-11. [4] 李光辉, 饶明军, 姜涛, 等.红土镍矿还原焙烧-磁选制取镍铁合金原料的新工艺[J].中国有色金属学报, 2011, 21(12):3137-3142. [5] 曹志成, 孙体昌, 杨慧芬, 等.红土镍矿直接还原焙烧磁选回收铁镍[J].北京科技大学学报, 2010, 32(6):708-712. [6] 郭爱克.神经计算科学[M].上海:上海科技出版社, 2001:69-81. [7] 孙超, 庞昕.BP神经网络优化微生物浸矿工艺[J].生物加工过程, 2012, 10(6):65-69. doi: 10.3969/j.issn.1672-3678.2012.06.014 [8] 张翼, 王旭东.基于正交试验和BP人工神经网络的浮选药剂制度优化[J].有色金属(选矿部分), 2018(2):99-102. doi: 10.3969/j.issn.1671-9492.2018.02.021 [9] 李好泽, 郭汉杰.红土镍矿预还原焙烧的研究[J].铁合金, 2014, 45(4):40-45. doi: 10.3969/j.issn.1001-1943.2014.04.010 [10] 赵景富, 席增宏, 王刚.红土镍矿回转窑还原焙烧指标影响因素研究[J].铁合金, 2014, 45(3):17-19, 23. doi: 10.3969/j.issn.1001-1943.2014.03.006 [11] 王德志, 汪异, 程仕平, 等.料层厚度对还原钼粉性能的影响[J].中国钼业, 2007, 31(3):20-22. doi: 10.3969/j.issn.1006-2602.2007.03.006 [12] 贺峰.红土镍矿直接还原焙烧-磁选试验研究[D].西安: 西安建筑科技大学, 2013: 33-35. -
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DANG Weiben, WANG Yubin, WANG Yan, WANG Xin. Condition Optimization of Reduction Roasting Magnetic Separation Technology for Laterite Nickel Ore by BP Neural Network Technique[J]. Conservation and Utilization of Mineral Resources, 2020, 40(5): 128-133. doi: 10.13779/j.cnki.issn1001-0076.2020.05.017
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Figure 1.
XRD pattern of laterite nickel samples
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Figure 2.
Test flow chart
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Figure 3.
Schematic diagram of three-layer neural network model
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Figure 4.
Model training error curve diagram
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Figure 5.
Effect of reductant quantity on the yield of nickel rough concentrate
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Figure 6.
Effect of roasting temperature on the yield of nickel rough concentrate
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Figure 7.
Effect of material thickness on the yield of nickel rough concentrate
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Figure 8.
Effect of roasting time on the yield of nickel rough concentrate
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Figure 9.
Effect of magnetic field strength on the yield of nickel rough concentrate